Spatial data viz

# Read in both the oil spill and CA counties data

ca_counties <- read_sf(here("data","ca_counties","CA_Counties_TIGER2016.shp")) %>% 
  clean_names()

oil_spills <- read_sf(here("data","oil","ds394.shp")) %>% 
  clean_names()

# Change the CRS of the spills data to match the CRS of the counties data
oil_spills <- st_transform(oil_spills, 3857)

# Creating an interactive map
tmap_mode(mode = "view")

tm_shape(ca_counties) +
  tm_fill("aland", palette = "BuGn") +
  tm_shape(oil_spills) +
  tm_dots()

# Making a graph for counts of inland oil spill events by county

# Begin by filtering the oil_spills subset 
inland_spills <- oil_spills %>% 
  filter(inlandmari=="Inland")

# Joining the filtered oil spills with the CA counties data
ca_spills <- ca_counties %>% 
  st_join(inland_spills)

# Finding the counts of oilspill events by county
county_counts <- ca_spills %>% 
  count(name)

ggplot(data = county_counts) +
  geom_sf(aes(fill = n), color = "white", size = 0.1) +
  scale_fill_gradientn(colors = c("lightgray","cyan","blue")) +
  theme_bw() +
  labs(fill = "Number of oil spills",
       title = "Counts of inland oil spill events by \ncounty in California (2008)")